Generative graphics and organic information design are making their way into the world of the applied arts: Last monday, I had the pleasure to attend Michael Schmitz' thesis presentation at the UDK digital media class. Mika has a history of meshing up biology and graphic design, for example breeding fonts in Genotyp. This time he took to the creation of dynamic logos and corporate identities, in this case the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG). They are very much concerned with how cells organize themselves to become tissue and other so-called emergent effects.
Looking for a suitable design solution, Mika soon learned about cellular automata, especially Conway's famous Game of Life, subject of many art pieces. His software basically follows the same rules in creating a dynamic logo for MPI-CBG in time, but the parameters are coupled to certain factors: number of employees = density, funding = speed, number of publications = activity. Different logos are being "bred" and then picked by fitness in relation to the parameters or voted for by the employees. Thus, everytime the logo is displayed on a website as an animated icon or printed out on a letter, it reflects the current state of the lab as a living organism.
Related: C.STEM Conference.
Why help biotech research firms design logos? Why design logos at all? How does this advance anything?
Why does design have to be"genetic?" Why does art have to be "generative?" What is it about random numbers that is so interesting?
Randomness is really a measure of compressibility, where sequences with predictable patterns are more compressible. The most random sequences are those with the least predictable patterns, which can't be described as frequently repeated pattern, and therefore can't be easily compressed.
But sometimes, random numbers are the byproducts of a predictable algorithm. Better understanding the nature of algorithmic processes could provide insights into ways working backwards - starting with a random sequence or event and understanding what algorithm was responsible. That means understanding mutations that cause disease, predicting the formation of hurricanes, or finding ways to reduce risks associated with market forces.
In truth, a lot of this generative computing stuff seems to be more thought experiment than science experiment - but if generative logos cause even one person to consider these paradigms for understanding, then they have been beneficial. Not that they need to be. If you don't care what's emblazoned on that double-cheeseburger you are eating, you can hardly criticize the Planck Institute.
It is actually the other way around. Compressibility is one measure of randomness. There are others. Check out Kolmogorov complexity if you want to understand this a bit more.